Snowflake connector#
The Snowflake connector allows querying and creating tables in an external Snowflake account. This can be used to join data between different systems like Snowflake and Hive, or between two different Snowflake accounts.
Configuration#
To configure the Snowflake connector, create a catalog properties file
in etc/catalog named, for example, example.properties, to
mount the Snowflake connector as the snowflake catalog.
Create the file with the following contents, replacing the
connection properties as appropriate for your setup:
connector.name=snowflake
connection-url=jdbc:snowflake://<account>.snowflakecomputing.com
connection-user=root
connection-password=secret
snowflake.account=account
snowflake.database=database
snowflake.role=role
snowflake.warehouse=warehouse
Arrow serialization support#
This is an experimental feature which introduces support for using Apache Arrow as the serialization format when reading from Snowflake. Please note there are a few caveats:
Using Apache Arrow serialization is disabled by default. In order to enable it, add
--add-opens=java.base/java.nio=ALL-UNNAMEDto the Trino JVM config.
Multiple Snowflake databases or accounts#
The Snowflake connector can only access a single database within a Snowflake account. Thus, if you have multiple Snowflake databases, or want to connect to multiple Snowflake accounts, you must configure multiple instances of the Snowflake connector.
General configuration properties#
The following table describes general catalog configuration properties for the connector:
Property name |
Description |
|---|---|
|
Support case insensitive schema and table names. Defaults to |
|
Duration for which case insensitive schema and table
names are cached. Defaults to |
|
Path to a name mapping configuration file in JSON format that allows
Trino to disambiguate between schemas and tables with similar names in
different cases. Defaults to |
|
Frequency with which Trino checks the name matching configuration file
for changes. The duration value defaults to |
|
Duration for which metadata, including table and
column statistics, is cached. Defaults to |
|
Cache the fact that metadata, including table and column statistics, is
not available. Defaults to |
|
Duration for which schema metadata is cached.
Defaults to the value of |
|
Duration for which table metadata is cached.
Defaults to the value of |
|
Duration for which tables statistics are cached.
Defaults to the value of |
|
Maximum number of objects stored in the metadata cache. Defaults to |
|
Maximum number of statements in a batched execution. Do not change
this setting from the default. Non-default values may negatively
impact performance. Defaults to |
|
Push down dynamic filters into JDBC queries. Defaults to |
|
Maximum duration for which Trino waits for dynamic
filters to be collected from the build side of joins before starting a
JDBC query. Using a large timeout can potentially result in more detailed
dynamic filters. However, it can also increase latency for some queries.
Defaults to |
Appending query metadata#
The optional parameter query.comment-format allows you to configure a SQL
comment that is sent to the datasource with each query. The format of this
comment can contain any characters and the following metadata:
$QUERY_ID: The identifier of the query.$USER: The name of the user who submits the query to Trino.$SOURCE: The identifier of the client tool used to submit the query, for exampletrino-cli.$TRACE_TOKEN: The trace token configured with the client tool.
The comment can provide more context about the query. This additional
information is available in the logs of the datasource. To include environment
variables from the Trino cluster with the comment , use the
${ENV:VARIABLE-NAME} syntax.
The following example sets a simple comment that identifies each query sent by Trino:
query.comment-format=Query sent by Trino.
With this configuration, a query such as SELECT * FROM example_table; is
sent to the datasource with the comment appended:
SELECT * FROM example_table; /*Query sent by Trino.*/
The following example improves on the preceding example by using metadata:
query.comment-format=Query $QUERY_ID sent by user $USER from Trino.
If Jane sent the query with the query identifier
20230622_180528_00000_bkizg, the following comment string is sent to the
datasource:
SELECT * FROM example_table; /*Query 20230622_180528_00000_bkizg sent by user Jane from Trino.*/
Note
Certain JDBC driver settings and logging configurations might cause the comment to be removed.
Domain compaction threshold#
Pushing down a large list of predicates to the data source can compromise
performance. Trino compacts large predicates into a simpler range predicate
by default to ensure a balance between performance and predicate pushdown.
If necessary, the threshold for this compaction can be increased to improve
performance when the data source is capable of taking advantage of large
predicates. Increasing this threshold may improve pushdown of large
dynamic filters.
The domain-compaction-threshold catalog configuration property or the
domain_compaction_threshold catalog session property can be used to adjust the default value of
256 for this threshold.
Case insensitive matching#
When case-insensitive-name-matching is set to true, Trino
is able to query non-lowercase schemas and tables by maintaining a mapping of
the lowercase name to the actual name in the remote system. However, if two
schemas and/or tables have names that differ only in case (such as “customers”
and “Customers”) then Trino fails to query them due to ambiguity.
In these cases, use the case-insensitive-name-matching.config-file catalog
configuration property to specify a configuration file that maps these remote
schemas/tables to their respective Trino schemas/tables:
{
"schemas": [
{
"remoteSchema": "CaseSensitiveName",
"mapping": "case_insensitive_1"
},
{
"remoteSchema": "cASEsENSITIVEnAME",
"mapping": "case_insensitive_2"
}],
"tables": [
{
"remoteSchema": "CaseSensitiveName",
"remoteTable": "tablex",
"mapping": "table_1"
},
{
"remoteSchema": "CaseSensitiveName",
"remoteTable": "TABLEX",
"mapping": "table_2"
}]
}
Queries against one of the tables or schemes defined in the mapping
attributes are run against the corresponding remote entity. For example, a query
against tables in the case_insensitive_1 schema is forwarded to the
CaseSensitiveName schema and a query against case_insensitive_2 is forwarded
to the cASEsENSITIVEnAME schema.
At the table mapping level, a query on case_insensitive_1.table_1 as
configured above is forwarded to CaseSensitiveName.tablex, and a query on
case_insensitive_1.table_2 is forwarded to CaseSensitiveName.TABLEX.
By default, when a change is made to the mapping configuration file, Trino must
be restarted to load the changes. Optionally, you can set the
case-insensitive-name-matching.config-file.refresh-period to have Trino
refresh the properties without requiring a restart:
case-insensitive-name-matching.config-file.refresh-period=30s
Non-transactional INSERT#
The connector supports adding rows using INSERT statements.
By default, data insertion is performed by writing data to a temporary table.
You can skip this step to improve performance and write directly to the target
table. Set the insert.non-transactional-insert.enabled catalog property
or the corresponding non_transactional_insert catalog session property to
true.
Note that with this property enabled, data can be corrupted in rare cases where exceptions occur during the insert operation. With transactions disabled, no rollback can be performed.
Type mapping#
Because Trino and Snowflake each support types that the other does not, this connector modifies some types when reading or writing data. Data types may not map the same way in both directions between Trino and the data source. Refer to the following sections for type mapping in each direction.
List of Snowflake data types.
Snowflake type to Trino type mapping#
The connector maps Snowflake types to the corresponding Trino types following this table:
Snowflake type |
Trino type |
Notes |
|---|---|---|
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Synonymous with |
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The names |
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Synonymous with |
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Default precision and scale are (38,0). |
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Synonymous with |
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Synonymous with |
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Synonymous with |
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Synonymous with |
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TIMESTAMP with no time zone; time zone, if provided, is not stored. See Snowflake Date & Time Data Types for more information. |
|
|
Alias for |
|
|
Alias for one of the |
|
|
TIMESTAMP with time zone. |
No other types are supported.
Trino type to Snowflake type mapping#
The connector maps Trino types to the corresponding Snowflake types following this table:
Trino type |
Snowflake type |
Notes |
|---|---|---|
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No other types are supported.
Type mapping configuration properties#
The following properties can be used to configure how data types from the connected data source are mapped to Trino data types and how the metadata is cached in Trino.
Property name |
Description |
Default value |
|---|---|---|
|
Configure how unsupported column data types are handled:
The respective catalog session property is |
|
|
Allow forced mapping of comma separated lists of data types to convert to
unbounded |
SQL support#
The connector provides read access and write access to data and metadata in a Snowflake database. In addition to the globally available and read operation statements, the connector supports the following features:
Procedures#
system.flush_metadata_cache()#
Flush JDBC metadata caches. For example, the following system call
flushes the metadata caches for all schemas in the example catalog
USE example.example_schema;
CALL system.flush_metadata_cache();
system.execute('query')#
The execute procedure allows you to execute a query in the underlying data
source directly. The query must use supported syntax of the connected data
source. Use the procedure to access features which are not available in Trino
or to execute queries that return no result set and therefore can not be used
with the query or raw_query pass-through table function. Typical use cases
are statements that create or alter objects, and require native feature such
as constraints, default values, automatic identifier creation, or indexes.
Queries can also invoke statements that insert, update, or delete data, and do
not return any data as a result.
The query text is not parsed by Trino, only passed through, and therefore only subject to any security or access control of the underlying data source.
The following example sets the current database to the example_schema of the
example catalog. Then it calls the procedure in that schema to drop the
default value from your_column on your_table table using the standard SQL
syntax in the parameter value assigned for query:
USE example.example_schema;
CALL system.execute(query => 'ALTER TABLE your_table ALTER COLUMN your_column DROP DEFAULT');
Verify that the specific database supports this syntax, and adapt as necessary based on the documentation for the specific connected database and database version.
Table functions#
The connector provides specific table functions to access Snowflake.
query(varchar) -> table#
The query function allows you to query the underlying database directly. It
requires syntax native to Snowflake, because the full query is pushed down and
processed in Snowflake. This can be useful for accessing native features which
are not available in Trino or for improving query performance in situations
where running a query natively may be faster.
Find details about the SQL support of Snowflake that you can use in the query in the Snowflake SQL Command Reference, including PIVOT, lateral joins and other statements and functions.
The native query passed to the underlying data source is required to return a table as a result set. Only the data source performs validation or security checks for these queries using its own configuration. Trino does not perform these tasks. Only use passthrough queries to read data.
As a simple example, query the example catalog and select an entire table:
SELECT
*
FROM
TABLE(
example.system.query(
query => 'SELECT
*
FROM
tpch.nation'
)
);
As a practical example, you can use the Snowflake SQL support for PIVOT to pivot on all distinct column values automatically with a dynamic pivot.
SELECT
*
FROM
TABLE(
example.system.query(
query => '
SELECT *
FROM quarterly_sales
PIVOT(SUM(amount) FOR quarter IN (ANY ORDER BY quarter))
ORDER BY empid;
'
)
);
Note
The query engine does not preserve the order of the results of this
function. If the passed query contains an ORDER BY clause, the
function result may not be ordered as expected.
Performance#
The connector includes a number of performance improvements, detailed in the following sections.
Pushdown#
The connector supports pushdown for a number of operations:
Aggregate pushdown for the following functions:
Note
The connector performs pushdown where performance may be improved, but in order to preserve correctness an operation may not be pushed down. When pushdown of an operation may result in better performance but risks correctness, the connector prioritizes correctness.